Troubling genderS and consumer well‐being: Going across, between and beyond the binaries to gender/sex/ual and intersectional diversity
Bibliographic record
Abstract
Abstract In this editorial we outline why a call for more inclusive, conscientious approaches to studying gender/sex/ual diversity and intersectional identities is needed, and how the articles in this special issue answered this call. We summarize key takeaways from a review of the literature, noting significant under‐representation of gender/sex/ual diversity and intersectional social locations. We also explore the history of the gender/sex binaries (e.g., female/male; women/men; femininity/masculinity) to help illuminate the premises upon which the popular trend of studying gender/sex differences between men and women and the invisibilities of gender/sex/ual diverse people exist. We conclude with guidance on how scholars and practitioners might engage in thinking, doing, and connecting to move the conversation forward.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".